THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Economic Control of Quality of Manufactured Product.

A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations

Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

Lecture Notes in Economics and Mathematical Systems

In this paper, we describe a taxonomy of generic graph related tasks and an evaluation aiming at assessing the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation bears on seven generic tasks and leads to important recommendations with regard to the representation of graphs according to their size and density. For instance, we show that when graphs are bigger than twenty vertices, the matrix-based visualization performs better than node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation

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A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

In this article, we describe a taxonomy of generic graph related tasks along with a computer-based evaluation designed to assess the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation encompasses seven generic tasks and leads to insightful recommendations for the representation of graphs according to their size and density. Typically, we show that when graphs are bigger than twenty vertices, the matrix-based visualization outperforms node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation.

On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis

Statistical process control plays a key role in today's highly competitive industrial environment since it allows quality practitioners to timely detect out-of-control situations and take actions whenever necessary in order to ensure that the products or services produced correspond to certain quality standards. Control charts are the tools quality practitioners use, and their monitoring performance is of major importance in practical applications. Since the values of the parameters used for the design of the charts' control limits are usually unknown in practice, the practitioners need to estimate them using an in-control retrospective sample. It has been shown that parameter estimation severely affects the control charts' properties. Many recent studies focused on investigating the impact of parameter estimates on the performance of control charts and on ways of diminishing this impact. This paper aims to provide an up-to-date critical review on the methodologies that have recently been developed in this area. Copyright © 2013 John Wiley & Sons, Ltd.

Some Recent Developments on the Effects of Parameter Estimation on Control Charts

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. This paper suggests a new method to monitor the CV.

Philippe Castagliola

Papers

A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis

Some Recent Developments on the Effects of Parameter Estimation on Control Charts

Monitoring the Coefficient of Variation Using EWMA Charts

An EWMA chart for monitoring the process standard deviation when parameters are estimated